There is a dearth of powerful, readily available computational tools for analyzing irregularly spaced data such as seizures and the effect of therapeutic interventions, and for intelligent optimization of therapies through quantitative analysis. The refinement and validation of a statistical methodology for analyzing the effects of an anti-seizure therapy (brain electrical stimulation or BES) are proposed. This application incorporates a statistical model that can elicit trends, influences of biological rhythms on seizure susceptibility and the effects of changes in treatment parameters on epileptic activity using the automated output of a proven seizure detection algorithm over the duration of a clinical study, thus leading to improved therapies. The method will be tested on a database of clinical BES trials for possible implementation as a software tool for analysis and optimization of therapy. An empirical seizure prediction algorithm (eSPA) incorporating trend extraction with empirical models will be developed using a similar framework to test the predictability of a subject's seizures. The eSPA will be evaluated on an extensive human database of long time series. The proposed methods will determine the predictability of a subject's seizures from a certain data set, and whether the eSPA can be implemented in a portable/implantable warning device.